Drafting
Karim Mezhoud
12/17/2018
The goal of this exercice is to decipher relevant knowledge from Basket DataSet.
Summarise basket object
# get the structure of the dataset
str(basket)## 'data.frame': 38765 obs. of 3 variables:
## $ Member_number : int 1808 2552 2300 1187 3037 4941 4501 3803 2762 4119 ...
## $ Date : Factor w/ 728 levels "01-01-2014","01-01-2015",..: 494 98 450 288 4 316 178 552 462 268 ...
## $ itemDescription: Factor w/ 167 levels "abrasive cleaner",..: 156 165 109 102 165 122 102 112 165 156 ...summary(basket)## Member_number Date itemDescription
## Min. :1000 21-01-2015: 96 whole milk : 2502
## 1st Qu.:2002 21-07-2015: 93 other vegetables: 1898
## Median :3005 08-08-2015: 92 rolls/buns : 1716
## Mean :3004 29-11-2015: 92 soda : 1514
## 3rd Qu.:4007 30-04-2015: 91 yogurt : 1334
## Max. :5000 26-03-2015: 88 root vegetables : 1071
## (Other) :38213 (Other) :28730The dataset is a dataframe with 3 columns: Member_number, Date, and Products description. The Member_number column must be considered as factor, like the id of member. The Date must be a date type. The itemDascription is already as.factor
convert the type of variables
library(magrittr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionbasket$Member_number <- as.factor(basket$Member_number)
basket$Date <- as.Date(basket$Date, "%d-%m-%Y")
# review the structure and summary
str(basket)## 'data.frame': 38765 obs. of 3 variables:
## $ Member_number : Factor w/ 3898 levels "1000","1001",..: 787 1505 1264 181 1982 3839 3407 2732 1712 3037 ...
## $ Date : Date, format: "2015-07-21" "2015-01-05" ...
## $ itemDescription: Factor w/ 167 levels "abrasive cleaner",..: 156 165 109 102 165 122 102 112 165 156 ...summary(basket)## Member_number Date itemDescription
## 3180 : 36 Min. :2014-01-01 whole milk : 2502
## 2051 : 33 1st Qu.:2014-07-12 other vegetables: 1898
## 3050 : 33 Median :2015-01-21 rolls/buns : 1716
## 3737 : 33 Mean :2015-01-09 soda : 1514
## 2271 : 31 3rd Qu.:2015-07-10 yogurt : 1334
## 2433 : 31 Max. :2015-12-30 root vegetables : 1071
## (Other):38568 (Other) :28730Now is better formated. We can observe: + The Member 3180 has the biggest operations (36). + The Whole milk product is the most sold with 2502 operations. + The sampling data was done during two years, between 2014-01-01 and 2015-12-30.
Before to start the exploration, It is important to check if the dataset countain empty cells or non available data.
check for any empty or NA cell
# Ckeck for Non Available (NA) cell
all(is.na(basket))## [1] FALSEThere is an intersting package named funModeling that make eeasy the exploration of Data analysis. We will use it.
library(funModeling) ## Loading required package: Hmisc## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, units## funModeling v.1.6.8 :)
## Examples and tutorials at livebook.datascienceheroes.comlibrary(tidyverse) ## ── Attaching packages ────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ tibble 1.4.2 ✔ purrr 0.2.5
## ✔ tidyr 0.8.2 ✔ stringr 1.3.1
## ✔ readr 1.2.1 ✔ forcats 0.3.0## ── Conflicts ───────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::extract() masks magrittr::extract()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::set_names() masks magrittr::set_names()
## ✖ Hmisc::src() masks dplyr::src()
## ✖ Hmisc::summarize() masks dplyr::summarize()library(Hmisc)
tibble::glimpse(basket)## Observations: 38,765
## Variables: 3
## $ Member_number <fct> 1808, 2552, 2300, 1187, 3037, 4941, 4501, 3803...
## $ Date <date> 2015-07-21, 2015-01-05, 2015-09-19, 2015-12-1...
## $ itemDescription <fct> tropical fruit, whole milk, pip fruit, other v...glimpse function returns informations like str.
funModeling::df_status(basket)## variable q_zeros p_zeros q_na p_na q_inf p_inf type unique
## 1 Member_number 0 0 0 0 0 0 factor 3898
## 2 Date 0 0 0 0 0 0 Date 728
## 3 itemDescription 0 0 0 0 0 0 factor 167df_statut allows us to detect abnormal values and if is there non available or empty cells.
freq(basket) ## Warning in freq_logic(data = data, input = input[i], plot, na.rm, path_out
## = path_out): Skipping plot for variable 'Member_number' (more than 100
## categories)## Member_number frequency percentage cumulative_perc
## 1 3180 36 0.09 0.09
## 2 2051 33 0.09 0.18
## 3 3050 33 0.09 0.27
## 4 3737 33 0.09 0.36
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## 3897 4994 2 0.01 102.85
## 3898 4998 2 0.01 100.00## Warning in freq_logic(data = data, input = input[i], plot, na.rm, path_out
## = path_out): Skipping plot for variable 'itemDescription' (more than 100
## categories)## itemDescription frequency percentage cumulative_perc
## 1 whole milk 2502 6.45 6.45
## 2 other vegetables 1898 4.90 11.35
## 3 rolls/buns 1716 4.43 15.78
## 4 soda 1514 3.91 19.69
## 5 yogurt 1334 3.44 23.13
## 6 root vegetables 1071 2.76 25.89
## 7 tropical fruit 1032 2.66 28.55
## 8 bottled water 933 2.41 30.96
## 9 sausage 924 2.38 33.34
## 10 citrus fruit 812 2.09 35.43
## 11 pastry 785 2.03 37.46
## 12 pip fruit 744 1.92 39.38
## 13 shopping bags 731 1.89 41.27
## 14 canned beer 717 1.85 43.12
## 15 bottled beer 687 1.77 44.89
## 16 whipped/sour cream 662 1.71 46.60
## 17 newspapers 596 1.54 48.14
## 18 frankfurter 580 1.50 49.64
## 19 brown bread 571 1.47 51.11
## 20 domestic eggs 566 1.46 52.57
## 21 pork 566 1.46 54.03
## 22 butter 534 1.38 55.41
## 23 fruit/vegetable juice 518 1.34 56.75
## 24 beef 516 1.33 58.08
## 25 curd 514 1.33 59.41
## 26 margarine 491 1.27 60.68
## 27 coffee 476 1.23 61.91
## 28 frozen vegetables 425 1.10 63.01
## 29 chicken 422 1.09 64.10
## 30 white bread 362 0.93 65.03
## 31 cream cheese 358 0.92 65.95
## 32 chocolate 357 0.92 66.87
## 33 dessert 356 0.92 67.79
## 34 napkins 335 0.86 68.65
## 35 berries 327 0.84 69.49
## 36 hamburger meat 327 0.84 70.33
## 37 UHT-milk 323 0.83 71.16
## 38 onions 305 0.79 71.95
## 39 salty snack 283 0.73 72.68
## 40 waffles 280 0.72 73.40
## 41 long life bakery product 269 0.69 74.09
## 42 sugar 265 0.68 74.77
## 43 butter milk 263 0.68 75.45
## 44 ham 256 0.66 76.11
## 45 meat 254 0.66 76.77
## 46 frozen meals 252 0.65 77.42
## 47 beverages 251 0.65 78.07
## 48 specialty chocolate 240 0.62 78.69
## 49 misc. beverages 238 0.61 79.30
## 50 ice cream 227 0.59 79.89
## 51 oil 225 0.58 80.47
## 52 grapes 220 0.57 81.04
## 53 hard cheese 220 0.57 81.61
## 54 candy 219 0.56 82.17
## 55 sliced cheese 212 0.55 82.72
## 56 specialty bar 210 0.54 83.26
## 57 hygiene articles 208 0.54 83.80
## 58 chewing gum 181 0.47 84.27
## 59 cat food 177 0.46 84.73
## 60 white wine 176 0.45 85.18
## 61 herbs 159 0.41 85.59
## 62 red/blush wine 157 0.41 86.00
## 63 processed cheese 152 0.39 86.39
## 64 soft cheese 152 0.39 86.78
## 65 flour 146 0.38 87.16
## 66 semi-finished bread 142 0.37 87.53
## 67 dishes 135 0.35 87.88
## 68 pickled vegetables 134 0.35 88.23
## 69 detergent 130 0.34 88.57
## 70 packaged fruit/vegetables 128 0.33 88.90
## 71 baking powder 122 0.31 89.21
## 72 pasta 121 0.31 89.52
## 73 pot plants 118 0.30 89.82
## 74 canned fish 116 0.30 90.12
## 75 seasonal products 106 0.27 90.39
## 76 liquor 103 0.27 90.66
## 77 frozen fish 102 0.26 90.92
## 78 spread cheese 100 0.26 91.18
## 79 condensed milk 98 0.25 91.43
## 80 cake bar 93 0.24 91.67
## 81 frozen dessert 92 0.24 91.91
## 82 mustard 92 0.24 92.15
## 83 salt 89 0.23 92.38
## 84 pet care 85 0.22 92.60
## 85 canned vegetables 82 0.21 92.81
## 86 roll products 82 0.21 93.02
## 87 turkey 80 0.21 93.23
## 88 photo/film 79 0.20 93.43
## 89 mayonnaise 75 0.19 93.62
## 90 cling film/bags 74 0.19 93.81
## 91 dish cleaner 73 0.19 94.00
## 92 frozen potato products 72 0.19 94.19
## 93 specialty cheese 72 0.19 94.38
## 94 flower (seeds) 70 0.18 94.56
## 95 sweet spreads 69 0.18 94.74
## 96 dog food 67 0.17 94.91
## 97 liquor (appetizer) 67 0.17 95.08
## 98 candles 66 0.17 95.25
## 99 finished products 64 0.17 95.42
## 100 instant coffee 61 0.16 95.58
## 101 chocolate marshmallow 60 0.15 95.73
## 102 Instant food products 60 0.15 95.88
## 103 zwieback 60 0.15 96.03
## 104 vinegar 51 0.13 96.16
## 105 liver loaf 50 0.13 96.29
## 106 rice 49 0.13 96.42
## 107 popcorn 48 0.12 96.54
## 108 soups 48 0.12 96.66
## 109 curd cheese 46 0.12 96.78
## 110 sparkling wine 46 0.12 96.90
## 111 house keeping products 45 0.12 97.02
## 112 sauces 44 0.11 97.13
## 113 cereals 42 0.11 97.24
## 114 softener 41 0.11 97.35
## 115 female sanitary products 40 0.10 97.45
## 116 spices 40 0.10 97.55
## 117 brandy 38 0.10 97.65
## 118 male cosmetics 37 0.10 97.75
## 119 meat spreads 35 0.09 97.84
## 120 jam 34 0.09 97.93
## 121 dental care 33 0.09 98.02
## 122 nuts/prunes 33 0.09 98.11
## 123 ketchup 32 0.08 98.19
## 124 rum 32 0.08 98.27
## 125 cleaner 31 0.08 98.35
## 126 kitchen towels 30 0.08 98.43
## 127 artif. sweetener 29 0.07 98.50
## 128 fish 29 0.07 98.57
## 129 light bulbs 29 0.07 98.64
## 130 specialty fat 29 0.07 98.71
## 131 snack products 27 0.07 98.78
## 132 tea 27 0.07 98.85
## 133 potato products 23 0.06 98.91
## 134 abrasive cleaner 22 0.06 98.97
## 135 nut snack 22 0.06 99.03
## 136 organic sausage 22 0.06 99.09
## 137 tidbits 22 0.06 99.15
## 138 canned fruit 21 0.05 99.20
## 139 syrup 21 0.05 99.25
## 140 skin care 20 0.05 99.30
## 141 soap 20 0.05 99.35
## 142 prosecco 19 0.05 99.40
## 143 bathroom cleaner 17 0.04 99.44
## 144 cookware 17 0.04 99.48
## 145 pudding powder 17 0.04 99.52
## 146 cocoa drinks 16 0.04 99.56
## 147 flower soil/fertilizer 16 0.04 99.60
## 148 cooking chocolate 15 0.04 99.64
## 149 ready soups 15 0.04 99.68
## 150 honey 13 0.03 99.71
## 151 cream 12 0.03 99.74
## 152 frozen fruits 11 0.03 99.77
## 153 specialty vegetables 11 0.03 99.80
## 154 organic products 10 0.03 99.83
## 155 decalcifier 9 0.02 99.85
## 156 hair spray 9 0.02 99.87
## 157 liqueur 9 0.02 99.89
## 158 whisky 8 0.02 99.91
## 159 salad dressing 6 0.02 99.93
## 160 frozen chicken 5 0.01 99.94
## 161 make up remover 5 0.01 99.95
## 162 rubbing alcohol 5 0.01 99.96
## 163 toilet cleaner 5 0.01 99.97
## 164 bags 4 0.01 99.98
## 165 baby cosmetics 3 0.01 99.99
## 166 kitchen utensil 1 0.00 99.99
## 167 preservation products 1 0.00 100.00## [1] "Variables processed: Member_number, itemDescription"library(Hmisc)
Hmisc::describe(basket)## basket
##
## 3 Variables 38765 Observations
## ---------------------------------------------------------------------------
## Member_number
## n missing distinct
## 38765 0 3898
##
## lowest : 1000 1001 1002 1003 1004, highest: 4996 4997 4998 4999 5000
## ---------------------------------------------------------------------------
## Date
## n missing distinct
## 38765 0 728
##
## lowest : 2014-01-01 2014-01-02 2014-01-03 2014-01-04 2014-01-05
## highest: 2015-12-26 2015-12-27 2015-12-28 2015-12-29 2015-12-30
## ---------------------------------------------------------------------------
## itemDescription
## n missing distinct
## 38765 0 167
##
## lowest : abrasive cleaner artif. sweetener baby cosmetics bags baking powder
## highest: white bread white wine whole milk yogurt zwieback
## ---------------------------------------------------------------------------Filter and screen the dataframe
At this step it is importante to know the number of Members and the number of products.
library(magrittr)
# How many member
base::table(basket$Member_number) %>% length()## [1] 3898# 3898
# How many operations
length(basket$Member_number)## [1] 38765# 38765
# How many products
base::table(basket$itemDescription) %>% length()## [1] 167# 167For all we have 3898 members and 167 products, and 38765 operations. This means that several members made several operations. How many operation per Members? And How many operations per product?
How many operations per Member
library(dplyr)
# How many transactions by Member, the data will be arranged by descending
basket %>% group_by(Member_number) %>% arrange(Member_number) %>% dplyr::summarise(N_operation=n()) %>% arrange(desc(N_operation))#basket_plot_operations <- basket %>% group_by(Member_number, Date) %>% summarise(N_operation=n()) %>% arrange(desc(N_operation))
#ggplot2::ggplot(basket_plot_operations[1:100,], aes_string(x = 'Date', y = 'N_operation')) +
# ggplot2::geom_bar(stat="identity", na.rm=TRUE)This table confirm that the Member 3180 ahs the biggest frequency of operations 36. We can see the first ten important Members which got the biggest numbers of operations.
# How many Operations were done by product during 2 Years. The data will be arranged by descending
basket %>% group_by(itemDescription) %>% dplyr::summarise(N_operations=n()) %>% arrange(desc(N_operations))This table shows arranges by descending the most important sold products.
We can visualize the first 50th best solds as following:
# How many transactions by product, the data will be arranged by descending
basket_plot_products <- basket %>% group_by(itemDescription) %>% dplyr::summarise(N_operations=n()) %>% arrange(desc(N_operations))
## plot the histogram of the best 50th sold products
ggplot2::ggplot(basket_plot_products[1:50,], aes(x = reorder(itemDescription, -N_operations), y = N_operations)) +
#aes_string(x = 'itemDescription', y = 'N_operations') ## without ordering
ggplot2::labs(title = "Overview of the first 50th best solds products during 2014-2015",
#fill = "",
x= "The products",
#colour= Date,
y = "The number of sales by product") +
ggplot2::theme(legend.title = element_text( colour="black",
size=11,
face="bold"),
title = element_text( size = 12,
face = 'bold'
),
text = element_text(size = 10,
face= 'bold'
),
axis.text.x=element_text(angle=45, hjust=1),
legend.position = "right",
legend.direction = "vertical"
) +
ggplot2::geom_bar(stat="identity", na.rm=TRUE)
# Monitoring the operations number were done per days, for all products during 2 years. The data will be arranged by descending
basket_plot_date <- basket %>% group_by(Date) %>% dplyr::summarise(N_operations=n()) # %>% arrange(desc(N_operations))
attach(basket_plot_date )
basket_plot_date %>%
ggplot(aes(x = Date, y = N_operations)) +
geom_point(color = "darkorchid4") +
geom_smooth(method = 'loess')+
labs(title = "Total Operations per day during the all period (2 years)",
subtitle = "Each value is the sum of all operations for all Members",
y = "Number of Operations",
x = "Date") + theme_bw(base_size = 15)
fit <- lm(N_operations~.,data= basket_plot_date)
plot(fit)
# library(tidyverse)
# basket <- read.csv("basket_bkp.csv", header = TRUE, sep = ",")
# basket$X <- NULL
# basket$Member_number <-as.factor(basket$Member_number)
# basket$Date <- as.Date(basket$Date, "%d-%m-%Y")
#
# basket.new <- mutate(basket, variable = rownames(basket)) %>%
# tidyr::gather(basket, Date, itemDescription)Regroupe all Operations by Month (December 2015)
## Determine the most 10 sold products
basket_products <- basket %>% group_by(itemDescription) %>% dplyr::summarise(N_operations=n()) %>% arrange(desc(N_operations))
basket_products10 <- as.data.frame(basket_products[1:10,])
basket %>% filter(itemDescription =="whole milk")## Regroup all sold products in December 2015 (add index for each operation)
#basket %>% #mutate(index = as.factor(seq.int(nrow(basket)))) %>%
#mutate(Month = format(Date, "%B"), Year = format(Date, "%Y")) %>% arrange(desc(Date)) %>% filter(Month == "December", #Year == "2015") %>% dplyr::mutate(N = dplyr::summarise( n()))
## add a new column with Month_Year
basket.Year_Month <- basket %>% mutate(Year_Month = substr(basket$Date, 1,7))
# basket <- read.csv("basket_bkp.csv", header = TRUE, sep = ",")
# basket$X <- NULL
# basket %>% mutate(Member_number = as.factor(Member_number))
# basket$Date <- as.Date(basket$Date, "%d-%m-%Y")
#basket$Year_Month <- zoo::as.yearmon(basket$Year_Month)
basket.Year_Month%>% dplyr::group_by(Year_Month) %>%
dplyr::summarise(N_Operations=n()) # Plot a subset of the data
#attach(Operations_Per_Month)
#ggplot(data = Operations_Per_Month, aes_string(x = zoo::as.yearmon(Year_Month), y = N_operations)) +
# geom_line(color = "#FC4E07")add index for each operation
basket %>% mutate(index = as.factor(seq.int(nrow(basket)))) Filter the 10 best sold products
basket_products <- basket %>% group_by(itemDescription) %>% dplyr::summarise(N_operations=n()) %>% arrange(desc(N_operations))
basket_products[1:10,]Explore if all operations for each Member were done in the same day
The idea is to associate products per basket for each Member.
## group operations by Member and filter it by date
basket %>% group_by(Member_number) %>% arrange(Member_number) %>% filter(Date=="2015-05-27")When We explore manually the table, we can see that some products sold together in the same date for the same Member. The response is: YES INITIATIVE TO PREDICT RULES BETWEEN PRODUCTS SOLD IN THE SAME TIME (BASKET: a group of products) IN SUPERMARKET FOR EXAMPLE.
combine all products per one Member
library(plyr)## -------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## -------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following object is masked from 'package:purrr':
##
## compact## The following objects are masked from 'package:Hmisc':
##
## is.discrete, summarize## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize#ddply(dataframe, variables_to_be_used_to_split_data_frame, function_to_be_applied)
transactionData_collapse_product <- plyr::ddply(basket,c("Member_number", "Date"),
function(basket)paste(basket$itemDescription,
collapse = ","))
transactionData_collapse_product# remove Customer ID as we do not need it
transactionData_collapse_product$Member_number <- NULL
# remove Date as we do not need it
transactionData_collapse_product$Date <- NULL
colnames(transactionData_collapse_product) <- c("Products")
write.csv(transactionData_collapse_product, file = "transaction.csv", quote = FALSE, row.names = FALSE)
transactionData_collapse_productPredict a rule between Product in each basket
Load transaction.csv to R, we will use read.transactions() function from Arules package this time.
library(arules)## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
##
## expand##
## Attaching package: 'arules'## The following object is masked from 'package:dplyr':
##
## recode## The following objects are masked from 'package:base':
##
## abbreviate, writetrans_basket <- read.transactions('transaction.csv', format = 'basket', sep=',') #, rm.duplicates=TRUE## Warning in asMethod(object): removing duplicated items in transactionsstr(trans_basket)## Formal class 'transactions' [package "arules"] with 3 slots
## ..@ data :Formal class 'ngCMatrix' [package "Matrix"] with 5 slots
## .. .. ..@ i : int [1:38007] 115 104 128 165 130 132 165 166 107 138 ...
## .. .. ..@ p : int [1:14965] 0 1 4 8 10 12 14 17 19 22 ...
## .. .. ..@ Dim : int [1:2] 168 14964
## .. .. ..@ Dimnames:List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : NULL
## .. .. ..@ factors : list()
## ..@ itemInfo :'data.frame': 168 obs. of 1 variable:
## .. ..$ labels: chr [1:168] "abrasive cleaner" "artif. sweetener" "baby cosmetics" "bags" ...
## ..@ itemsetInfo:'data.frame': 0 obs. of 0 variablestrans_basket## transactions in sparse format with
## 14964 transactions (rows) and
## 168 items (columns)summary(trans_basket) # call in summary to check our data## transactions as itemMatrix in sparse format with
## 14964 rows (elements/itemsets/transactions) and
## 168 columns (items) and a density of 0.01511843
##
## most frequent items:
## whole milk other vegetables rolls/buns soda
## 2363 1827 1646 1453
## yogurt (Other)
## 1285 29433
##
## element (itemset/transaction) length distribution:
## sizes
## 1 2 3 4 5 6 7 8 9 10
## 206 10012 2727 1273 338 179 113 96 19 1
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 2.00 2.54 3.00 10.00
##
## includes extended item information - examples:
## labels
## 1 abrasive cleaner
## 2 artif. sweetener
## 3 baby cosmeticsWe can explore the data and make the frequency plot using the itemFrequencyPlot() function in the arules package. For this plot, we will check the top 10 products with absolute & relative frequency. The two plots give the same bihavior.
par(mfrow=c(1,2))
itemFrequencyPlot(trans_basket, topN=10, type='absolute', main="Absolute Product Frequency Plot")
itemFrequencyPlot(trans_basket, topN=10, type='relative', main="Relative Product Frequency Plot")
Modeling and Evaluation
We can start by mining the data for overall association rules.
Refering to our item frequency plots, let’s try to set minimum support at 1 in 1,000 operations and minimum confidence at 70 percent. Also we set the maximum number of Products to be associated as two
basket_rules <- arules::apriori(trans_basket, parameter = list(supp = 0.01, conf = 0.8)) #, maxlen=5## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.01 1
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 149
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[168 item(s), 14964 transaction(s)] done [0.00s].
## sorting and recoding items ... [64 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [0 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules <- sort(basket_rules, by='confidence', decreasing = TRUE)
summary(rules)## set of 0 rulesNote that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.